Two image-based sensing methods are merged to mimic human vision in support of airborne detect-and-avoid and counter–unmanned aircraft systems applications. In the proposed sensing system architecture, a peripheral vision camera (with a fisheye lens) provides a large field of view, whereas a central vision camera (with a perspective lens) provides high-resolution imagery of a specific target. Beyond the complementary ability of the two cameras and supporting algorithms to enable passive detection and classification, the pair forms a heterogeneous stereo vision system that can support range resolution. The paper describes development and testing of a novel peripheral–central vision system to detect, localize, and classify an airborne threat. The system was used to generate a dataset for various types of mock threats in order to experimentally validate parametric analysis of the threat localization error. A system performance analysis based on Monte Carlo simulations is also described, providing further insight concerning the effect of system parameters on threat localization accuracy.

两种基于图像的传感方法被合并以模仿人类的视觉，以支持机载侦查和躲避以及反无人机系统的应用。在所提出的感测系统架构中，外围视觉相机（带有鱼眼镜头）提供了较大的视野，而中央视觉相机（带有鱼眼镜头）提供了特定目标的高分辨率图像。除了两台摄像机的互补能力和支持无源检测和分类的支持算法外，这对还形成了可以支持测距分辨率的异构立体视觉系统。本文介绍了一种新型外围-中央视觉系统的开发和测试，以检测，定位和分类空中威胁。该系统用于生成各种类型的模拟威胁的数据集，以便通过实验验证威胁定位错误的参数分析。还描述了基于蒙特卡洛模拟的系统性能分析，从而提供有关系统参数对威胁定位准确性的影响的进一步见解。